Multifunctional MXene/rGO aerogels loaded with Co/MnO nanocomposites for enhanced electromagnetic wave absorption,thermal insulation and pressure sensing

Aerogel-based composites hold promising application prospects as potential electromagnetic wave(EMW)absorption materials,yet the construction of such materials with ingenious microstructures,appropriate magnetic/dielectric multi-components,and integrated multifunctionality remains considerably challenging.Herein,a multicomponent Co/MnO/Ti_(3)C_(2)T_(x) MXene/rGO(CMMG)hybrid aerogel featured with three-dimensional(3D)vertical directional channel architecture is reported.Benefiting from the synergistic effect arising from the 3D conductive networking structure,diverse heterogeneous interfaces,magnetic/dielectric multicomponent,and multiple loss pathways,the optimized CMMG-2 aerogel delivers fascinating EMW absorption capabilities,characterized by a minimal reflection loss(RL_(min))of-77.41 dB and an effective absorption bandwidth(EAB)of 6.56 GHz.Additionally,the remarkable hydrophobicity,exceptional thermal insulation capabilities,and outstanding photothermal properties of CMMG-2 aerogel make it highly promising for multiple application in diverse and demanding environments.Interestingly,the distinctive pore structure of hybrid aerogel also allows it for sensitive and reliable detection of electrical signals caused by pressure changes and human motion.Thus,this research provides a viable design strategy for the development of lightweight,efficient,and multifunctional aerogel-based EMW absorption materials for various application scenarios.

Multi-objective vehicle rebalancing for ridehailing system using a reinforcement learning approach

The problem of designing a rebalancing algorithm for a large-scale ridehailing system with asymmetric(unbalanced)demand is considered here.We pose the rebalancing problem within a semi Markov decision problem(SMDP)framework with closed queues of vehicles serving stationary,but asymmetric demand,over a large city with multiple stations(representing neighborhoods).We assume that the passengers queue up at every station until they are matched with a vehicle.The goal of the SMDP is to minimize a convex combination of the waiting time of the passengers and the total empty vehicle miles traveled.The resulting SMDP appears to be difficult to solve yielding closed-form expression for the optimal rebalancing strategy.Consequently,we use a deep reinforcement learning algorithm to determine the approximately optimal solution to the SMDP.We show through extensive Monte Carlo simulations that the trained policy outperforms other well-known state-dependent rebalancing strategies.